import os
from os.path import isfile, basename, abspath
import xarray as xr
import numpy as np
from datetime import datetime
from pathlib import Path
import hvplot
import hvplot.xarray
import earthaccessFrom the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.
Working with SWOT Level 2 Water Mask Raster Image Data Product:
Local Machine Download Version
Summary & Learning Objectives
Notebook showcasing how to work with multiple files from the SWOT Raster Image data product version 2.0 on a local machine
- Utilizing the
earthaccessPython package. For more information visit: https://nsidc.github.io/earthaccess/ - Option to query the new dataset based on user’s choice; choosing between two resolutions either by ‘100m’ or ‘250m’.
- Visualizing multiple raster images on a single map.
- Stacking multiple raster images and creating a time dimension to analyze over time.
- Adjusting images based on quality flag
Requirements
1. Compute environment
This tutorial is written to run in the following environment: - Local compute environment e.g. laptop, server: this tutorial can be run on your local machine
2. Earthdata Login
An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.
Import libraries
Authentication with earthaccess
In this notebook, we will be calling the authentication in the below cell.
auth = earthaccess.login()Search for SWOT Raster products using earthaccess
Each dataset has it’s own unique collection concept ID. For the SWOT_L2_HR_Raster_2.0 dataset, we can find the collection ID here.
For this tutorial, we are looking at the Lake Mead Reservoir in the United States.
We used bbox finder to get the exact coordinates for our area of interest.
results = earthaccess.search_data(
short_name = 'SWOT_L2_HR_RASTER_2.0',
bounding_box=(-115.112686,35.740939,-114.224167,36.937819),
temporal =('2024-02-01 12:00:00', '2024-02-01 23:59:59'),
granule_name = '*_100m_*',
count =200
)Granules found: 2
earthaccess.download(results, "data_downloads/SWOT_Raster_LakeMead/") Getting 2 granules, approx download size: 0.07 GB
Accessing cloud dataset using dataset endpoint credentials: https://archive.swot.podaac.earthdata.nasa.gov/s3credentials
Downloaded: data_downloads/SWOT_Raster_LakeMead/SWOT_L2_HR_Raster_100m_UTM11S_N_x_x_x_010_218_045F_20240201T183814_20240201T183835_PIC0_01.nc
Downloaded: data_downloads/SWOT_Raster_LakeMead/SWOT_L2_HR_Raster_100m_UTM11S_N_x_x_x_010_218_046F_20240201T183834_20240201T183855_PIC0_01.nc
['data_downloads/SWOT_Raster_LakeMead/SWOT_L2_HR_Raster_100m_UTM11S_N_x_x_x_010_218_045F_20240201T183814_20240201T183835_PIC0_01.nc',
'data_downloads/SWOT_Raster_LakeMead/SWOT_L2_HR_Raster_100m_UTM11S_N_x_x_x_010_218_046F_20240201T183834_20240201T183855_PIC0_01.nc']
Visualizing Multiple Tiles
Let’s now visualize multiple raster tiles in a folder and explore the data.
Utilizing xarray.open_mfdataset which supports the opening of multiple files.
folder_path = "data_downloads/SWOT_Raster_LakeMead/"
file_paths = [os.path.join(folder_path, file) for file in os.listdir(folder_path) if file.endswith('.nc')]ds = xr.open_mfdataset(file_paths, combine='nested', concat_dim='x')
ds<xarray.Dataset>
Dimensions: (y: 2784, x: 3074)
Coordinates:
* y (y) float64 3.899e+06 3.899e+06 ... 4.177e+06
* x (x) float64 5.682e+05 5.683e+05 ... 6.928e+05
Data variables: (12/39)
crs (x) object b'1' b'1' b'1' b'1' ... b'1' b'1' b'1'
longitude (y, x) float64 dask.array<chunksize=(512, 512), meta=np.ndarray>
latitude (y, x) float64 dask.array<chunksize=(512, 512), meta=np.ndarray>
wse (y, x) float32 dask.array<chunksize=(768, 768), meta=np.ndarray>
wse_qual (y, x) float32 dask.array<chunksize=(2784, 1536), meta=np.ndarray>
wse_qual_bitwise (y, x) float64 dask.array<chunksize=(768, 768), meta=np.ndarray>
... ...
load_tide_fes (y, x) float32 dask.array<chunksize=(768, 768), meta=np.ndarray>
load_tide_got (y, x) float32 dask.array<chunksize=(768, 768), meta=np.ndarray>
pole_tide (y, x) float32 dask.array<chunksize=(768, 768), meta=np.ndarray>
model_dry_tropo_cor (y, x) float32 dask.array<chunksize=(768, 768), meta=np.ndarray>
model_wet_tropo_cor (y, x) float32 dask.array<chunksize=(768, 768), meta=np.ndarray>
iono_cor_gim_ka (y, x) float32 dask.array<chunksize=(768, 768), meta=np.ndarray>
Attributes: (12/49)
Conventions: CF-1.7
title: Level 2 KaRIn High Rate Raster Data Product
source: Ka-band radar interferometer
history: 2024-02-05T08:50:33Z : Creation
platform: SWOT
references: V1.2.1
... ...
x_min: 568200.0
x_max: 721700.0
y_min: 3898600.0
y_max: 4052100.0
institution: CNES
product_version: 01- yPandasIndex
PandasIndex(Float64Index([3898600.0, 3898700.0, 3898800.0, 3898900.0, 3899000.0, 3899100.0, 3899200.0, 3899300.0, 3899400.0, 3899500.0, ... 4176000.0, 4176100.0, 4176200.0, 4176300.0, 4176400.0, 4176500.0, 4176600.0, 4176700.0, 4176800.0, 4176900.0], dtype='float64', name='y', length=2784)) - xPandasIndex
PandasIndex(Float64Index([568200.0, 568300.0, 568400.0, 568500.0, 568600.0, 568700.0, 568800.0, 568900.0, 569000.0, 569100.0, ... 691900.0, 692000.0, 692100.0, 692200.0, 692300.0, 692400.0, 692500.0, 692600.0, 692700.0, 692800.0], dtype='float64', name='x', length=3074))
- Conventions :
- CF-1.7
- title :
- Level 2 KaRIn High Rate Raster Data Product
- source :
- Ka-band radar interferometer
- history :
- 2024-02-05T08:50:33Z : Creation
- platform :
- SWOT
- references :
- V1.2.1
- reference_document :
- JPL D-56416 - Revision C - December 8, 2023
- contact :
- podaac@podaac.jpl.nasa.gov
- cycle_number :
- 10
- pass_number :
- 218
- scene_number :
- 46
- tile_numbers :
- [90 91 92 93 90 91 92 93]
- tile_names :
- 218_090L, 218_091L, 218_092L, 218_093L, 218_090R, 218_091R, 218_092R, 218_093R
- tile_polarizations :
- H, H, H, H, V, V, V, V
- coordinate_reference_system :
- Universal Transverse Mercator
- resolution :
- 100.0
- short_name :
- L2_HR_Raster
- descriptor_string :
- 100m_UTM11S_N_x_x_x
- crid :
- PIC0
- pge_name :
- PGE_L2_HR_RASTER
- pge_version :
- 5.1.1
- time_granule_start :
- 2024-02-01T18:38:34.265833Z
- time_granule_end :
- 2024-02-01T18:38:55.360807Z
- time_coverage_start :
- 2024-02-01T18:38:34.807712Z
- time_coverage_end :
- 2024-02-01T18:38:54.816495Z
- geospatial_lon_min :
- -116.24045666354958
- geospatial_lon_max :
- -114.55674281329392
- geospatial_lat_min :
- 35.225727504637376
- geospatial_lat_max :
- 36.5951883116925
- left_first_longitude :
- -114.84459394683485
- left_first_latitude :
- 36.5951883116925
- left_last_longitude :
- -114.55674281329392
- left_last_latitude :
- 35.46460890403489
- right_first_longitude :
- -116.24045666354958
- right_first_latitude :
- 36.350992807631336
- right_last_longitude :
- -115.93433361296329
- right_last_latitude :
- 35.225727504637376
- xref_l2_hr_pixc_files :
- SWOT_L2_HR_PIXC_010_218_090L_20240201T183824_20240201T183835_PIC0_01.nc, SWOT_L2_HR_PIXC_010_218_091L_20240201T183834_20240201T183845_PIC0_01.nc, SWOT_L2_HR_PIXC_010_218_092L_20240201T183844_20240201T183855_PIC0_01.nc, SWOT_L2_HR_PIXC_010_218_093L_20240201T183854_20240201T183905_PIC0_01.nc, SWOT_L2_HR_PIXC_010_218_090R_20240201T183824_20240201T183835_PIC0_01.nc, SWOT_L2_HR_PIXC_010_218_091R_20240201T183834_20240201T183845_PIC0_01.nc, SWOT_L2_HR_PIXC_010_218_092R_20240201T183844_20240201T183855_PIC0_01.nc, SWOT_L2_HR_PIXC_010_218_093R_20240201T183854_20240201T183905_PIC0_01.nc
- xref_l2_hr_pixcvec_files :
- SWOT_L2_HR_PIXCVec_010_218_090L_20240201T183824_20240201T183835_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_218_091L_20240201T183834_20240201T183845_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_218_092L_20240201T183844_20240201T183855_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_218_093L_20240201T183854_20240201T183905_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_218_090R_20240201T183824_20240201T183835_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_218_091R_20240201T183834_20240201T183845_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_218_092R_20240201T183844_20240201T183855_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_218_093R_20240201T183854_20240201T183905_PIC0_01.nc
- xref_param_l2_hr_raster_file :
- SWOT_Param_L2_HR_Raster_20000101T000000_21000101T000000_20230817T100000_v302.rdf
- xref_reforbittrack_files :
- SWOT_RefOrbitTrackTileBoundary_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt, SWOT_RefOrbitTrack125mPass1_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt, SWOT_RefOrbitTrack125mPass2_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt
- utm_zone_num :
- 11
- mgrs_latitude_band :
- S
- x_min :
- 568200.0
- x_max :
- 721700.0
- y_min :
- 3898600.0
- y_max :
- 4052100.0
- institution :
- CNES
- product_version :
- 01